A Hybrid Approach for Smart Crop Health Monitoring Using Deep Learning and IoT

The rapid advancement of autonomous technology has completely changed agricultural operations by providing new ways to monitor crop health and manage resources more efficiently. Automated crop health monitoring and early disease detection have been made possible by integrating artificial intelligence (AI) and the Internet of Things (IoT), which has greatly enhanced precision agriculture. This study presented a hybrid AI-IoT approach that combined deep learning-based image detection with sensor-driven soil and environmental monitoring. DL models such as Convolutional Neural Networks (CNNs) and YOLOvll were used to categorize crop diseases and IoT -enabled sensors tracked vital indicators like humidity, temperature, and soil moisture. The proposed system achieved a training accuracy of 94% and a precision accuracy of 95%, demonstrating its efficiency in disease detection. The results also showed that incorporating AI-based disease detection with real-time environmental data has enhanced decision-making for farmers. This approach decreased the need for labor inspection and increased agricultural productivity by giving immediate insights into crop conditions. In practical applications, these results demonstrate how AI-driven agricultural solutions can enhance crop health management, reduce losses, and advance sustainable farming methods.